Abstract

Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings. This is done using adaptive processing and unsupervised clustering methods applied to neural recordings to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
PublisherIEEE Computer Society
Pages851-855
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove
Period11/6/1611/9/16

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Maurer, A., Hanrahan, S., Nedrud, J., Hebb, A. O., & Papandreou-Suppappola, A. (2017). Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG. In Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 851-855). [7869169] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869169